Epidemic forecasting with a new fuzzy regression equation

The traditional fuzzy regression model involves two solving processes. First, the extension principle is used to derive the membership function of extrapolated values, and then, attempts are made to include every collected value with a membership degree of at least h in the fuzzy regression interval. However, the membership function of extrapolated values is sometimes highly complex, and it is difficult to determine the h value, i.e., the degree of fit between the input values and the extrapolative fuzzy output values, when the information obtained from the collected data is insufficient. To solve this problem, we proposed a simplified fuzzy regression equation based on Carlsson and Fullér’s possibilistic mean and variance method and used it for modeling the constraints and objective function of a fuzzy regression model without determining the membership function of extrapolative values and the value of h. Finally, we demonstrated the application of our model in forecasting pneumonia mortality. Thus, we verified the effectiveness of the proposed model and confirmed the potential benefits of our approach, in which the forecasting error is very small.     

Bayesian median autoregression for robust time series forecasting

We develop a Bayesian median autoregressive (BayesMAR) model for time series forecasting. The proposed method utilizes time-varying quantile regression at the median, favorably inheriting the robustness of median regression in contrast to the widely used mean-based methods. Motivated by a working Laplace likelihood approach in Bayesian quantile regression, BayesMAR adopts a parametric model bearing the same structure as autoregressive models by altering the Gaussian error to Laplace, leading to a simple, robust, and interpretable modeling strategy for time series forecasting. We estimate model parameters by Markov chain Monte Carlo. Bayesian model averaging is used to account for model uncertainty, including the uncertainty in the autoregressive order, in addition to a Bayesian model selection approach. The proposed methods are illustrated using simulations and real data applications. An application to U.S. macroeconomic data forecasting shows that BayesMAR leads to favorable and often superior predictive performance compared to the selected mean-based alternatives under various loss functions that encompass both point and probabilistic forecasts. The proposed methods are generic and can be used to complement a rich class of methods that build on autoregressive models.     

Time-varying sparsity in dynamic regression models

A novel Bayesian method for inference in dynamic regression models is proposed where both the values of the regression coefficients and the importance of the variables are allowed to change over time. We focus on forecasting and so the parsimony of the model is important for good performance. A prior is developed which allows the shrinkage of the regression coefficients to suitably change over time and an efficient Markov chain Monte Carlo method for posterior inference is described. The new method is applied to two forecasting problems in econometrics: equity premium prediction and inflation forecasting. The results show that this method outperforms current competing Bayesian methods.     

Improving forecasting performance using covariate-dependent copula models

Copulas provide an attractive approach to the construction of multivariate distributions with flexible marginal distributions and different forms of dependences. Of particular importance in many areas is the possibility of forecasting the tail-dependences explicitly. Most of the available approaches are only able to estimate tail-dependences and correlations via nuisance parameters, and cannot be used for either interpretation or forecasting. We propose a general Bayesian approach for modeling and forecasting tail-dependences and correlations as explicit functions of covariates, with the aim of improving the copula forecasting performance. The proposed covariate-dependent copula model also allows for Bayesian variable selection from among the covariates of the marginal models, as well as the copula density. The copulas that we study include the Joe-Clayton copula, the Clayton copula, the Gumbel copula and the Student’s $t$-copula. Posterior inference is carried out using an efficient MCMC simulation method. Our approach is applied to both simulated data and the S&P 100 and S&P 600 stock indices. The forecasting performance of the proposed approach is compared with those of other modeling strategies based on log predictive scores. A value-at-risk evaluation is also performed for the model comparisons.     

Density forecasting with Bayesian Vector Autoregressive models under macroeconomic data uncertainty

Macroeconomic data are subject to data revisions. Yet, the usual way of generating real-time density forecasts from Bayesian Vector Autoregressive (BVAR) models makes no allowance for data uncertainty from future data revisions. We develop methods of allowing for data uncertainty when forecasting with BVAR models with stochastic volatility. First, the BVAR forecasting model is estimated on real-time vintages. Second, the BVAR model is jointly estimated with a model of data revisions such that forecasts are conditioned on estimates of the ‘true’ values. We find that this second method generally improves upon conventional practice for density forecasting, especially for the United States.     

Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting,incorporating realized measures

This paper extends the joint Value-at-Risk (VaR) and expected shortfall (ES) quantile regression model of Taylor (2019), by incorporating a realized measure to drive the tail risk dynamics, as a potentially more efficient driver than daily returns. Furthermore, we propose and test a new model for the dynamics of the ES component. Both a maximum likelihood and an adaptive Bayesian Markov chain Monte Carlo method are employed for estimation, the properties of which are compared in a simulation study. The results favour the Bayesian approach, which is employed subsequently in a forecasting study of seven financial market indices. The proposed models are compared to a range of parametric, non-parametric and semi-parametric competitors, including GARCH, realized GARCH, the extreme value theory method and the joint VaR and ES models of Taylor (2019), in terms of the accuracy of one-day-ahead VaR and ES forecasts, over a long forecast sample period that includes the global financial crisis in 2007–2008. The results are favorable for the proposed models incorporating a realized measure, especially when employing the sub-sampled realized variance and the sub-sampled realized range.     

An empirical analysis of international labour migration in the Philippines

This study empirically investigates the impact of economic, demographic, and political factors on the size of emigration from the Philippines. In 2007, overseas workers from the Philippines sent remittances in excess of US$14 billion annually to their families back home. Although these remittances are an important source of foreign exchange and play an important role in economic development, the determinants of emigration in the Philippines are not well established. A simple unrestricted error correction model of migration was specified and estimated using data spanning the period 1975–2005. Results indicate that the level of unemployment, adult literacy and population density are the key determinants of emigration in the Philippines. The result also indicates that government instability impacts negatively on emigration in the Philippines. The policy implications of the results are discussed.     

Threshold cointegration in international exchange rates:A Bayesian approach

This paper considers Bayesian estimation of the threshold vector error correction (TVECM) model in moderate to large dimensions. Using the lagged cointegrating error as a threshold variable gives rise to additional difficulties that typically are solved by utilizing large sample approximations. By relying on Markov chain Monte Carlo methods, we are enabled to circumvent these issues and avoid computationally-prohibitive estimation strategies like the grid search. Due to the proliferation of parameters, we use novel global-local shrinkage priors in the spirit of Griffin and Brown (2010). We illustrate the merits of our approach in an application to five exchange rates vis-á-vis the US dollar by means of a forecasting comparison. Our findings indicate that adopting a non-linear modeling approach improves the predictive accuracy for most currencies relative to a set of simpler benchmark models and the random walk.     

Could we have predicted the recent downturn in the South African housing market?

This paper develops large-scale Bayesian Vector Autoregressive (BVAR) models, based on 268 quarterly series, for forecasting annualized real house price growth rates for large-, medium- and small-middle-segment housing for the South African economy. Given the in-sample period of 1980:01–2000:04, the large-scale BVARs, estimated under alternative hyperparameter values specifying the priors, are used to forecast real house price growth rates over a 24-quarter out-of-sample horizon of 2001:01–2006:04. The forecast performance of the large-scale BVARs are then compared with classical and Bayesian versions of univariate and multivariate Vector Autoregressive (VAR) models, merely comprising of the real growth rates of the large-, medium- and small-middle-segment houses, and a large-scale Dynamic Factor Model (DFM), which comprises of the same 268 variables included in the large-scale BVARs. Based on the one- to four-quarters-ahead Root Mean Square Errors (RMSEs) over the out-of-sample horizon, we find the large-scale BVARs to not only outperform all the other alternative models, but to also predict the recent downturn in the real house price growth rates for the three categories of the middle-segment-housing over the period of 2003:01–2008:02.     

Forecasting television ratings

Despite the state of flux in media today, television remains the dominant player globally for advertising spending. Since television advertising time is purchased on the basis of projected future ratings, and ad costs have skyrocketed, there is increasingly pressure to forecast television ratings accurately. The forecasting methods that have been used in the past are not generally very reliable, and many have not been validated; also, even more distressingly, none have been tested in today’s multichannel environment. In this study we compare eight different forecasting models, ranging from a naïve empirical method to a state-of-the-art Bayesian model-averaging method. Our data come from a recent time period, namely 2004-2008, in a market with over 70 channels, making the data more typical of today’s viewing environment. The simple models that are commonly used in industry do not forecast as well as any econometric models. Furthermore, time series methods are not applicable, as many programs are broadcast only once. However, we find that a relatively straightforward random effects regression model often performs as well as more sophisticated Bayesian models in out-of-sample forecasting. Finally, we demonstrate that making improvements in ratings forecasts could save the television industry between $250 and $586 million per year.     

From sending to host societies: immigration in Greece,Ireland and Spain in the 21st century

In a relatively short period of time, new immigration patterns have changed the geography of immigration of the EU15, bringing three old emigration countries, Ireland, Spain and Greece, to the forefront of the new immigration wave. This article studies the analogies and differences of the recent immigration experience of this group of countries, focusing on the demographic characteristics of the immigrants (origin, sex, gender and education), labour market insertion (wages, labour market segregation and quality of matching) and overall economic performance in terms of poverty rates.     

Migration: the trends converge

Formerly, Australia, New Zealand, Canada, and the US have served as permanent destinations for immigrants, while Europe's migrants have moved to more northerly countries to work for a time and then returned home. From 1973-1975 Europe's recruitment of foreign workers virtually ended, although family reunion for those immigrants allowed in was encouraged. Problems resulting from this new settlement migration include low paying jobs for immigrant women, high unemployment, and inadequate education for immigrant children. Illegal migrants from Latin America and the Caribbean enter the US and Canada each year while illegal North African immigrants enter Italy, Spain, and Greece. North America, Australia, and Europe have all received political refugees from Asia and Latin America. Increasingly, these foreigners compete in the labor market rather than simply fill jobs the native workers do not want. All the receiving countries have similar policy priorities: 1) more effective ways for controlling and monitoring inflows and checking illegal immigration; 2) encouraging normal living patterns and accepting refugees; and 3) integrating permanent migrants into the host country. Europe's public immigration encouragement prior to the first oil shock, has left some countries with a labor force that is reluctant to return home. It is unlikely that Europe will welcome foreign labor again in this decade, since unemployment among young people and women is high and family reunion programs may still bring in many immigrants. Less immigration pattern change will probably occur in North America, Australia, and New Zealand since these countries' populations are still growing and wages are more flexible. Immigration, regulated by policy, and emigration, determined by market forces, now are working in the same direction and will likely reduce future migration flows.     

Selecting stochastic mortality models for the Italian population

The future revision of capital requirements and a market-consistent valuation of non-hedgeable liabilities lead to an increasing attention on forecasting longevity trends. In this field, many methodologies focus on either modeling mortality or pricing mortality-linked securities (as longevity bonds). Following Lee–Carter method (proposed in 1992), actuarial literature has provided several extensions in order to consider different trends observed in European data set (e.g., the cohort effect). The purpose of the paper is to compare the features of main mortality models proposed over the years. Model selection became indeed a primary task with the aim to identify the “best” model. What is meant by best is controversial, but good selection techniques are usually based on a good balance between goodness of fit and simplicity. In this regard, different criteria, mainly based on residual and projected rates analysis, are here used. For the sake of comparison, main forecasting methods have been applied to deaths and exposures to risk of male Italian population. Weaknesses and strengths have been emphasized, by underlying how various models provide a different goodness of fit according to different data sets. At the same time, the quality and the variability of forecasted rates have been compared by evaluating the effect on annuity values. Results confirm that some models perform better than others, but no single model can be defined as the best method.     

基于模糊时间序列的预测模型——以上证指数为例

模糊理论使用语义变量本身所蕴含的特性,能减少处理问题时的不确定性所带来的困扰,被广泛的应用于各种领域的研究。首先回顾了基于模糊理论的模糊时间序列定义,对现有的模糊时间序列模型进行分析;在此基础上提出了一种新的模糊时间序列预测方法,以上证指数为对象进行了拟合。从结果看,新的基于模糊时间序列预测方法在MSN、平均误差(%)和标准误差(%)等指标上要优于现有的的预测方法。     

A comprehensive evaluation of macroeconomic forecasting methods

We employ datasets for seven developed economies and consider four classes of multivariate forecasting models in order to extend and enhance the empirical evidence in the macroeconomic forecasting literature. The evaluation considers forecasting horizons of between one quarter and two years ahead. We find that the structural model, a medium-sized DSGE model, provides accurate long-horizon US and UK inflation forecasts. We strike a balance between being comprehensive and producing clear messages by applying meta-analysis regressions to 2,976 relative accuracy comparisons that vary with the forecasting horizon, country, model class and specification, number of predictors, and evaluation period. For point and density forecasting of GDP growth and inflation, we find that models with large numbers of predictors do not outperform models with 13–14 hand-picked predictors. Factor-augmented models and equal-weighted combinations of single-predictor mixed-data sampling regressions are a better choice for dealing with large numbers of predictors than Bayesian VARs.     

Inference and prediction in a multiple-structural-break model

This paper develops a new Bayesian approach to structural break modeling. The focuses of the approach are the modeling of in-sample structural breaks and forecasting time series allowing out-of-sample breaks. The model has several desirable features. First, the number of regimes is not fixed but is treated as a random variable. Second, the model adopts a hierarchical prior for regime coefficients, which allows for the coefficients of one regime to contain information about coefficients of other regimes. Third, the regime coefficients can be integrated analytically in the posterior density; as a consequence the posterior simulator is fast and reliable. An application to US real GDP quarterly growth rates links groups of regimes to specific historical periods and provides forecasts of future growth rates.     

Forecasting inflation using time‐varying Bayesian model averaging

This paper presents a Bayesian model averaging regression framework for forecasting US inflation, in which the set of predictors included in the model is automatically selected from a large pool of potential predictors and the set of regressors is allowed to change over time. Using real‐time data on the 1960–2011 period, this model is applied to forecast personal consumption expenditures and gross domestic product deflator inflation. The results of this forecasting exercise show that, although it is not able to beat a simple random‐walk model in terms of point forecasts, it does produce superior density forecasts compared with a range of alternative forecasting models. Moreover, a sensitivity analysis shows that the forecasting results are relatively insensitive to prior choices and the forecasting performance is not affected by the inclusion of a very large set of potential predictors.     

Forecasting economic activity with mixed frequency BVARs

Mixed frequency Bayesian vector autoregressions (MF-BVARs) allow forecasters to incorporate large numbers of time series that are observed at different intervals into forecasts of economic activity. This paper benchmarks the performances of MF-BVARs for forecasting U.S. real gross domestic product growth against surveys of professional forecasters and documents the influences of certain specification choices. We find that a medium–large MF-BVAR provides an attractive alternative to surveys at the medium-term forecast horizons that are of interest to central bankers and private sector analysts. Furthermore, we demonstrate that certain specification choices influence its performance strongly, such as model size, prior selection mechanisms, and modeling in levels versus growth rates.     

基于轻量级工作流建模的研究与设计

业务过程建模是整个工作流管理系统的基础,一种高效灵活的建模技术对工作流管理系统起着至关重要的作用。笔者通过研究Petri网等现有的工作流建模技术,结合工作流系统的实际需求,提出了基于节点、迁移的轻量级工作流建模设想,并通过一个实际工作流建模过程进行了实践和验证。